An improved region growing algorithm in 3D laser point cloud identification of rock mass structural plane

Abstract

The rock mass structural plane constitutes the weakest part of the rock mass. Accurate and efficient identification of rock mass structural plane and extraction of characteristic information can provide an important basis for the rock mass stability evaluation. 3D laser scanning technology can greatly improve the efficiency and accuracy of structural surface survey; however, the current mainstream point cloud analysis algorithms exist the problems that the edge recognition of structural surfaces is blurred and the accuracy of point cloud segmentation cannot meet the accuracy of structural surface feature information extraction. Considering the spatial relationship between the position of the point cloud of the rock mass structural plane and its neighborhood, the region growth segmentation parameters were corrected by multiple eigenvalues. The KD-tree data structure was used to perform the nearest neighbor search. The voxel was sampled, and the structural plane was segmented to realize the extraction of the structure plane occurrence, spacing, and extension information, based on the normal vector difference of the point cloud and the characteristic final value. The effectiveness of this method in structural plane identification was also verified by indoor models. The results show that compared with the traditional Principal Component Analysis method and Random Sample Consensus method, this method has a higher recognition rate and accuracy in the same area among the 24 structural planes composed of indoor block models. It can not only ensure the complete recognition of data in the complex and changing plane area, but also better segment the edge points in the sharp position of the plane. Using this method, 24 structural planes can be divided into 6 groups, and the corresponding structural plane feature information can be obtained. Compared with the actual measurement results, the angle information error is approximately 1°, and the distance information error is within 1 cm. This method identified three groups of structural planes in the Mangshezhai slope rock mass successfully in the main stream of the Yangtze River. The method proposed in this study has a good verification effect on indoor model and field slope, which can provide robust and effective technical support for the identification and segmentation of rock mass structural plane.

Keywords

• three-dimensional laser scanning
• point cloud information extraction
• region growing algorithm
• intelligently identifies
• rock mass structural plane